Ketan Paranjape is the general manager, Life Sciences at Intel Corporation. Along with managing an Intel-wide multi-disciplinary team, his role involves developing business and markets, strategies and solutions for insurance companies, hospitals and clinics, governments, academic and research institutions, wearable companies and pharmaceuticals.

At Intel, Paranjape has been part of products spanning the entire “Compute Continuum” from high-performance computing (HPC) to embedded medical devices in roles encompassing architecture, R&D, strategy, planning, sales and marketing. He has served as chief of staff at Intel Research and technical advisor to the chief technology officer and is currently a faculty member at the International Institute for Analytics and teaches at the Harvard School of Public Health.

Q: What is the state of genomic sequencing today?

A: Popularly referred to as next-generation sequencing (NGS), or high-throughput sequencing, NGS is the catch-all term used to describe a number of different modern sequencing technologies. This has allowed us to sequence DNA and RNA much faster and cheaper than the previously used Sanger sequencing.

The cost of genomic sequencing has also come a long way. From $3 billion to sequence the first human genome, to about $100 million per genome in 2001, and as of January 2014, the cost is about $1,000.

The issue now is computing power to analyze this data. Newer sequencers are now producing four times the data in half the time. Intel® technologies like Xeon® and Xeon® Phi®, SSDs, 10/40 GbE networking solutions, Omni-Path fabric interconnect, Intel Enterprise Edition for Lustre (IEEL), along with partners like Cloudera and Amazon Web Services, are helping to cut down the time for secondary analysis from weeks to hours.

Genomic information is now catalogued and used for advancing precision medicine. For example – genomic information from TCGA (The Cancer Genome Atlas) has led to developments and FDA approval for certain cancer treatments. Currently, there are about 34 FDA-approved targeted therapies like Gleevec that treats gastrointestinal stromal tumors by blocking tyrosine kinase enzymes. Though approved by the FDA in 2001, it was further granted efficacy to treat 10 more types of cancers in 2011.

Q: What are the technical challenges in bringing genomics mainstream?

A: Big data has arrived in this space. Sequencers are now producing four times more data in 50 percent less time at about 0.5TB/device/day. This is a lot of data. Newer modalities like 4-D imaging are now producing 2 TB/device/day. The majority of the software used for informatics and analytics is open sourced and the market is very fragmented.

Once the data is generated, the burden of storing, managing, sharing, ingesting and moving it has its own set of challenges.

Innovation in algorithms and techniques is outpacing what IT can support, thus requiring flexibility and agility in infrastructures.

Finally, as genomics makes its way into clinics, clinical guidelines like HIPAA will kick in.

Q: What are some of the other challenges with bringing genomics into the clinics?

A: At the clinical level, you have barriers around the conservation and validity of the sample, validity and repeatability of laboratory results, novelty and interpretation of biomarkers, merging genomics data with clinical data, actionability and eventually changing the healthcare delivery paradigm.

There are too few clinical specialists and key healthcare professionals, like pharmacists, who are trained in clinical genomics. New clinical pathways and guidelines will have to be created. Systems will need to be put in place to increase transparency and accountability of different stakeholders of genomic data usage. Equality and justice needs to be ensured and protection against discrimination needs to be put in place (GINA).

Q: How do we merge genomic data with EHRs?

A: We need to develop a standardized genetic terminology and make sure EHRs support the ability to browse sequenced data. Current EHRs will need standards around communication, querying, storing and compressing large volumes of data while interfacing with EHRs’ identifiable patient information.

Intel is partnering with Intermountain Health to create a new set of Clinical Decision Support (CDS) applications by combining clinical, genomic and family health history data. The goal is to promote widespread use of CDS that will help clinicians/counselors in assessing risk and assist genetic counselors in ordering genetic tests.

The solution will be agnostic to data collection tools, scale to different clinical domains and other healthcare institutions, be standards based where they exist, work across all EHRs, leverage state-of-the-art technologies and be flexible to incorporate other data sources.